Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Eidnes, Sølve"'
Autor:
Wu, Jie, Eidnes, Sølve, Jin, Jingzhe, Lie, Halvor, Yin, Decao, Passano, Elizabeth, Sævik, Svein, Riemer-Sorensen, Signe
Publikováno v:
Journal of Fluids and Structures, Volume 116, 2023, 103793
Offshore slender marine structures experience complex and combined load conditions from waves, current and vessel motions that may result in both wave frequency and vortex shedding response patterns. Field measurements often consist of records of env
Externí odkaz:
http://arxiv.org/abs/2406.18611
Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. Conventional single model approaches often struggle to capture the interplay of diverse dynamics, resulting in s
Externí odkaz:
http://arxiv.org/abs/2403.02150
We introduce the mean inverse integrator (MII), a novel approach to increase the accuracy when training neural networks to approximate vector fields of dynamical systems from noisy data. This method can be used to average multiple trajectories obtain
Externí odkaz:
http://arxiv.org/abs/2306.03548
Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. By assuming a certain pseudo
Externí odkaz:
http://arxiv.org/abs/2305.06920
Autor:
Eidnes, Sølve, Lye, Kjetil Olsen
Pseudo-Hamiltonian neural networks (PHNN) were recently introduced for learning dynamical systems that can be modelled by ordinary differential equations. In this paper, we extend the method to partial differential equations. The resulting model is c
Externí odkaz:
http://arxiv.org/abs/2304.14374
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving. We introduce a pseudo-Hamiltonian formulation that is a generalizat
Externí odkaz:
http://arxiv.org/abs/2206.02660
Autor:
Eidnes, Sølve, Lye, Kjetil Olsen
Publikováno v:
In Journal of Computational Physics 1 March 2024 500
Autor:
Eidnes, Sølve
The discrete gradient methods are integrators designed to preserve invariants of ordinary differential equations. From a formal series expansion of a subclass of these methods, we derive conditions for arbitrarily high order. We derive specific resul
Externí odkaz:
http://arxiv.org/abs/2003.08267
Autor:
Eidnes, Sølve, Li, Lu
We present linearly implicit methods that preserve discrete approximations to local and global energy conservation laws for multi-symplectic PDEs with cubic invariants. The methods are tested on the one-dimensional Korteweg-de Vries equation and the
Externí odkaz:
http://arxiv.org/abs/1907.02122
Kahan's method and a two-step generalization of the discrete gradient method are both linearly implicit methods that can preserve a modified energy for Hamiltonian systems with a cubic Hamiltonian. These methods are here investigated and compared. Th
Externí odkaz:
http://arxiv.org/abs/1901.03573